Landscape-scale drivers of pollinator communities may depend on land-use configuration
Bibliographic record
Abstract
Research into pollinators in managed landscapes has recently combined approaches of pollination ecology and landscape ecology, because key stressors are likely to interact across wide areas. While laboratory and field experiments are valuable for furthering understanding, studies are required to investigate the interacting drivers of pollinator health and diversity across a broader range of landscapes and a wider array of taxa. Here, we use a network of 96 study landscapes in six topographically diverse regions of Britain, to test the combined importance of honeybee density, insecticide loadings, floral resource availability and habitat diversity to pollinator communities. We also explore the interactions between these drivers and the cover and proximity of semi-natural habitat. We found that among our four drivers, only honeybee density was positively related to wild pollinator abundance and diversity, and the positive association between abundance and floral resources depended on insecticide loadings and habitat diversity. By contrast, our exploratory models including habitat composition metrics revealed a complex suite of interactive effects. These results demonstrate that improving pollinator community composition and health is unlikely to be achieved with general resource enhancements only. Rather, local land-use context should be considered in fine-tuning pollinator management and conservation. This article is part of the theme issue 'Natural processes influencing pollinator health: from chemistry to landscapes'.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".